Varol Akman

Anaphora resolution is one of the most active research areas in natural language processing. This study examines focusing as a tool for the resolution of pronouns which are a kind of anaphora. Focusing is a discourse phenomenon like anaphora. Candy Sidner formalized focusing in her 1979 MIT PhD thesis and devised several algorithms to resolve definite anaphora including pronouns. She presented her theory in a computational framework but did not generally implement the algorithms. Her algorithms related to focusing and pronoun resolution are implemented in this paper. This implementation provides a better comprehension of the theory both from a conceptual and a computational point of view. The resulting program is tested on different discourse segments, and evaluation and analysis of the experiments are presented together with the statistical results.

Founded in 1993, ELEKTRIK: Turkish Journal of Electrical Engineering and Computer Sciences, has gradually become better known and is fast establishing itself as a research oriented publication outlet with high academic standards. In a modest attempt to advance this trend, this special issue of ELEKTRIK brings together five papers exemplifying the state of the art in artificial intelligence (AI). Written by experts, the papers are especially aimed at readers interested in gaining a better appraisal of the applications side of the AI enterprise. In all papers there is a strong emphasis on measuring the benefits of the proposed approaches in experimental contexts. The papers broadly fall into five actively researched, contemporary domains of AI: pattern recognition, genetic algorithms, fuzzy sets, intelligent design, and agents.

In Strawson’s Entity and Identity, there are two essays (Chapters 11 and 12), which study the notion of context. In these essays, Strawson advances a threefold distinction regarding how context bears on the meaning of 'what is said' when a sentence is uttered. In this paper, we'll (i) review the original scheme of Strawson and summarize his improvements to his own scheme, and (ii) add our own improvements to make it even more thoroughgoing. We'll also show that unless it is elaborated with several considerations (mostly based on our work regarding context as a social construct and contextualizing as a form of social action) it cannot function as a realistic initiative towards building common sense models of how intended meaning is achieved.

[This is a review of: Drew McDermott, Mind and Mechanism, MIT Press, Cambridge, MA, 2001.] The author is interested in computational approaches to consciousness. His reason for working in the field of AI is to solve the mind-body problem, that is, to understand how the brain can have experiences. This is an intricate project because it involves elucidation of the relationship between our mentality and its physical foundation. How can a biological/chemical system (the human body) have experiences, beliefs, desires, intentions, and so on? Physicists have good reasons to persuade us that ours is a material world that obeys physical laws. Once we commit ourselves to this view, it sounds quite bewildering to think that there is a place for independently existing minds in such a world. When physicists speak, McDermott listens. His hypothesis is that we are all contraptions designed by evolution. The concept of mind arises because people’s brains are biological computers. Crudely put, minds are produced by brains. The essential contribution of this rich book is an extended argument about how one can be more specific about the way this production is realized.

On Crimmins and Perry’s account of propositional attitude ascription (1989), beliefs are concrete cognitive structures—particulars ("things in the head") that belong to an agent and that have a lifetime. They are related to the world and to other cognitive structures and abilities, allowing one to classify the latter by propositional content. Containing ideas and notions as constituents, beliefs are structured entities. The difference between notions and ideas is the difference between an agent’s ways of thinking about individuals vs. properties.